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import streamlit as st
import tensorflow as tf
from PIL import Image
import numpy as np
import cv2
from huggingface_hub import from_pretrained_keras
try:
model=from_pretrained_keras("SerdarHelli/Segmentation-of-Teeth-in-Panoramic-X-ray-Image-Using-U-Net")
except:
model=tf.keras.models.load_model("dental_xray_seg.h5")
pass
st.header("Segmentation of Teeth in Panoramic X-ray Image Using UNet")
examples=["107.png","108.png","109.png"]
link='Check Out Our Github Repo ! [link](https://github.com/SerdarHelli/Segmentation-of-Teeth-in-Panoramic-X-ray-Image-Using-U-Net)'
st.markdown(link,unsafe_allow_html=True)
def load_image(image_file):
img = Image.open(image_file)
return img
def convert_one_channel(img):
#some images have 3 channels , although they are grayscale image
if len(img.shape)>2:
img= cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
return img
else:
return img
def convert_rgb(img):
#some images have 3 channels , although they are grayscale image
if len(img.shape)==2:
img= cv2.cvtColor(img,cv2.COLOR_GRAY2RGB)
return img
else:
return img
st.subheader("Upload Dental Panoramic X-ray Image Image")
image_file = st.file_uploader("Upload Images", type=["png","jpg","jpeg"])
col1, col2, col3 = st.columns(3)
with col1:
ex=load_image(examples[0])
st.image(ex,width=200)
if st.button('Example 1'):
image_file=examples[0]
with col2:
ex1=load_image(examples[1])
st.image(ex1,width=200)
if st.button('Example 2'):
image_file=examples[1]
with col3:
ex2=load_image(examples[2])
st.image(ex2,width=200)
if st.button('Example 3'):
image_file=examples[2]
if image_file is not None:
img=load_image(image_file)
st.text("Making A Prediction ....")
st.image(img,width=850)
img=np.asarray(img)
img_cv=convert_one_channel(img)
img_cv=cv2.resize(img_cv,(512,512), interpolation=cv2.INTER_LANCZOS4)
img_cv=np.float32(img_cv/255)
img_cv=np.reshape(img_cv,(1,512,512,1))
prediction=model.predict(img_cv)
predicted=prediction[0]
predicted = cv2.resize(predicted, (img.shape[1],img.shape[0]), interpolation=cv2.INTER_LANCZOS4)
mask=np.uint8(predicted*255)#
_, mask = cv2.threshold(mask, thresh=0, maxval=255, type=cv2.THRESH_BINARY+cv2.THRESH_OTSU)
kernel =( np.ones((5,5), dtype=np.float32))
mask=cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel,iterations=1 )
mask=cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel,iterations=1 )
cnts,hieararch=cv2.findContours(mask,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
output = cv2.drawContours(convert_rgb(img), cnts, -1, (255, 0, 0) , 3)
if output is not None :
st.subheader("Predicted Image")
st.write(output.shape)
st.image(output,width=850)
st.text("DONE ! ....")
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